We’ve covered this topic before, but it is always good to mention in again. Howard Goodall asks this on Twitter:

“Ever wondered why climate scientists use anomalies instead of temperatures? 100 years of catastrophic warming in central England has the answer.”

He provides a link to the Central England Temperature data at the Met Office and a plot made from that data, which just happens to be in absolute degrees C as opposed to the usual anomaly plot:

Now compare that to the anomaly based plot for the same data from the Met Office:

The CET anomaly data is here, format example here.

Goodall has a point, that without using anomalies and magnified scales, it would be difficult to detect “climate change”.

For example, annual global mean NASA GISS temperature data displayed as an anomaly plot:

Source: https://data.giss.nasa.gov/gistemp/graphs/

Data: https://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt

Now here is the same data, through 2016, plotted as absolute temperature, as you’d see on a thermometer, without the scale amplification, using the scale of human experience with temperature on the planet:

h/t to “Suyts” for the plot. His method is to simply add the 1951-1980 baseline temperature declared by GISS (57.2 deg F) back to the anomaly temperature, to recover the absolute temperature, then plot it. GISS provides the method here:

Q. What do I do if I need absolute SATs, not anomalies?
A. In 99.9% of the cases you’ll find that anomalies are exactly what you need, not absolute temperatures. In the remaining cases, you have to pick one of the available climatologies and add the anomalies (with respect to the proper base period) to it. For the global mean, the most trusted models produce a value of roughly 14°C, i.e. 57.2°F, but it may easily be anywhere between 56 and 58°F and regionally, let alone locally, the situation is even worse.

What is even more interesting, is the justification GISS makes for using anomalies:

The GISTEMP analysis concerns only temperature anomalies, not absolute temperature. Temperature anomalies are computed relative to the base period 1951-1980. The reason to work with anomalies, rather than absolute temperature is that absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region. Indeed, we have shown (Hansen and Lebedeff, 1987) that temperature anomalies are strongly correlated out to distances of the order of 1000 km.

And this is why, even though there are huge missing gaps in data, as shown here: (note the poles)

Note: Gray areas signify missing data.
Note: Ocean data are not used over land nor within 100km of a reporting land station.

GISS can “fill in” (i.e. make up) data where there isn’t any using 1200 kilometer smoothing: (note the poles, magically filled in, and how the cold stations in the graph above on the perimeter on Antarctica, disappear in this plot)

Note: Gray areas signify missing data.
Note: Ocean data are not used over land nor within 100km of a reporting land station.

It’s interesting how they can make the south pole red, and if it’s burning hot, when in reality, the average mean temperature is approximately -48°C (–54.4°F):

The source: https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show.cgi?id=700890090008&dt=1&ds=5

Based on GHCN data from NOAA-NCEI and data from SCAR.

• GHCN-Unadjusted is the raw data as reported by the weather station.
• GHCN-adj is the data after the NCEI adjustment for station moves and breaks.
• GHCN-adj-cleaned is the adjusted data after removal of obvious outliers and less trusted duplicate records.
• GHCN-adj-homogenized is the adjusted, cleaned data with the GISTEMP removal of an urban-only trend.

It’s all in the presentation.

NASA GISS helps us see red in Antarctica (while erasing the perimeter blues) at -48°C (–54.4°F), thanks to anomalies and 1200 kilometer smoothing (because “temperature anomalies are strongly correlated out to distances of the order of 1000 km”).

Now that’s what I call polar amplification.

Let the Stokes-Mosher caterwauling begin.